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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Zhaozheng Yin Collins, R.T. |
| Copyright Year | 2008 |
| Description | Author affiliation: Dept. of Comput. Sci. & Eng., Pennsylvania State Univ., University Park, PA (Zhaozheng Yin; Collins, R.T.) |
| Abstract | Given an object model and a black-box measure of similarity between the model and candidate targets, we consider visual object tracking as a numerical optimization problem. During normal tracking conditions when the object is visible from frame to frame, local optimization is used to track the local mode of the similarity measure in a parameter space of translation, rotation and scale. However, when the object becomes partially or totally occluded, such local tracking is prone to failure, especially when common prediction techniques like the Kalman filter do not provide a good estimate of object parameters in future frames. To recover from these inevitable tracking failures, we consider object detection as a global optimization problem and solve it via adaptive simulated annealing (ASA), a method that avoids becoming trapped at local modes and is much faster than exhaustive search. As a Monte Carlo approach, ASA stochastically samples the parameter space, in contrast to local deterministic search. We apply cluster analysis on the sampled parameter space to redetect the object and renew the local tracker. Our numerical hybrid local and global mode-seeking tracker is validated on challenging airborne videos with heavy occlusion and large camera motions. Our approach outperforms state-of-the-art trackers on the VIVID benchmark datasets. |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 1365186 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424422425 |
| ISSN | 10636919 |
| DOI | 10.1109/CVPR.2008.4587542 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-06-23 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Object detection Target tracking Rotation measurement Extraterrestrial measurements Parameter estimation Optimization methods Simulated annealing Monte Carlo methods Videos Cameras |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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